Takashi Nicholas Maeda, Shohei Shimizu. Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data. CoRR. 2024. abs/2401.07231
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, and Shohei Shimizu. Python package for causal discovery based on LiNGAM. Journal of Machine Learning Research. 2023. 24. 14
Masatomo Suzuki, Junichiro Mori, Takashi Nicholas Maeda, Jun Ikeda. The economic value of urban landscapes in a suburban city of Tokyo, Japan: A semantic segmentation approach using Google Street View images. Journal of Asian Architecture and Building Engineering. 2022. 22. 3. 1110-1125
Takashi Nicholas MAEDA. I-RCD: An improved algorithm of repetitive causal discovery from data with latent confounders. Behaviormetrika. 2022
Takashi Nicholas MAEDA, Shohei SHIMIZU. Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. International Journal of Data Science and Analytics. 2021. 13. 2. 77-89
